{"id":3053,"date":"2017-05-23T22:48:54","date_gmt":"2017-05-24T05:48:54","guid":{"rendered":"http:\/\/mattfife.com\/?p=3053"},"modified":"2017-05-23T22:48:54","modified_gmt":"2017-05-24T05:48:54","slug":"gradient-descent-using-flocking-algorithms","status":"publish","type":"post","link":"https:\/\/mattfife.com\/?p=3053","title":{"rendered":"Gradient Descent using Flocking algorithms"},"content":{"rendered":"

\"\"<\/p>\n

One of the key mathematical foundations of machine learning is using gradient descent to find maxima and minima in a multi-dimensional data set. Gradient descent is good, but getting the most out of it can sometimes leave you wringing your hands or doing a lot of painful mathematical investigation and analysis. Investigations that can quickly tax even a mathematics major.<\/p>\n

Sergui\u00a0Puscas shows us a different, more intuitive way<\/a> to find maxima and minima by using swarming and flocking techniques. It’s a pretty fun read.<\/p>\n","protected":false},"excerpt":{"rendered":"

One of the key mathematical foundations of machine learning is using gradient descent to find maxima and minima in a multi-dimensional data set. Gradient descent is good, but getting the most out of it can sometimes leave you wringing your hands or doing a lot of painful mathematical investigation and analysis. Investigations that can quickly tax even a mathematics major. Sergui\u00a0Puscas shows us a different, more intuitive way to find maxima and minima by using swarming and flocking techniques. It’s…<\/p>\n

Read More Read More<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[7,5],"tags":[],"class_list":["post-3053","post","type-post","status-publish","format-standard","hentry","category-technicalprogramming","category-technical"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4WECr-Nf","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts\/3053","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3053"}],"version-history":[{"count":1,"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts\/3053\/revisions"}],"predecessor-version":[{"id":3055,"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts\/3053\/revisions\/3055"}],"wp:attachment":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3053"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3053"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3053"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}